Regression-Based Geometric Partitioning Mode Coding

Geometric Partitioning Mode (GPM) is an effective coding tool for inter prediction that splits a block into two partitions and blends their predictions. This paper presents a new coding mode, Regression-based Geometric Partitioning Mode (RGPM), which derives a sample-based blending for bi-predictions using a reconstructed template. The RGPM can enhance flexibility in splitting and blending methods compared to GPM. Moreover, two extensions of RGPM scheme are investigated: 1) extending RGPM with template matching (TM) and merge with motion vector difference (MMVD) methods; 2) extending RGPM principle to Spatial Geometric Partitioning Mode (SGPM) for intra prediction. Experimental results show that RGPM with extensions provide 0.12%, 0.24% and 0.23% average luma BD-rate savings on top of enhanced compression model (ECM) in all intra, random access and low delay configurations, respectively. The proposed RGPM is currently adopted in ECM and its two extensions are under study in exploration experiments for future ECM developments.